Concept

Bug (engineering)

Publications associées (4)

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Nirupam Gupta, John Stephan, Sadegh Farhadkhani, Le Nguyen Hoang, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

FuZZan: Efficient Sanitizer Metadata Design for Fuzzing

Mathias Josef Payer

Fuzzing is one of the most popular and effective techniques for finding software bugs. To detect triggered bugs, fuzzers leverage a variety of sanitizers in practice. Unfortunately, sanitizers target long running experiments-e.g., developer test suites-not ...

USENIX ASSOC2020

S2E: A Platform for In-Vivo Multi-Path Analysis of Software Systems

George Candea, Vitaly Chipounov, Volodymyr Kuznetsov

This paper presents S2E, a platform for analyzing the properties and behavior of software systems. We demonstrate S2E's use in developing practical tools for comprehensive performance profiling, reverse engineering of proprietary software, and bug finding ...

2011

Selective Symbolic Execution

George Candea, Cristian Zamfir, Vitaly Chipounov

Symbolic execution is a powerful technique for analyzing program behavior, finding bugs, and generating tests, but suffers from severely limited scalability: the largest programs that can be symbolically executed today are on the order of thousands of line ...

2009

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